Data Visualization Techniques to Prep for Machine Learning Models

Master advanced data visualization with Matplotlib & Seaborn. Learn expert techniques to create stunning, insightful charts that make data clear and compelling.

Machine learning runs on data, so before we jump into fancy models, you need to really understand the data you’re working with.

Think about it like the launch of the first iPad in 2010—Apple didn’t just slap a touchscreen on a slab and hope for the best… or did they?

Probably not, they tested, analyzed user behavior, and optimized everything before releasing it. The same goes for machine learning. Before running any algorithms, we need to explore, clean, and understand your data—otherwise, you’re just launching a glitchy prototype into the world.

That’s where Exploratory Data Analysis (EDA) comes in.

This is all apart of my Machine Learning series. While everyone else gets just a taste, premium readers get the whole feast! Don't miss out on the full experience!

In this article, we’ll break down the EDA process step by step. We’ll look at distributions, trends, and how different variables relate to each other. To make it practical, we’ll use real-world data—maybe sales numbers or stock market trends—to pull out useful business insights.

EDA is all about digging into your data to see what’s going on. It helps you spot patterns, trends, and relationships while also catching missing values, outliers, and anything that might mess up your model.

By taking the time to explore the dataset, you’ll get a better feel for how different variables connect, which ones actually matter, and what kind of cleanup needs to be done.

Not only does this week article build into the next segment of our Machine Learning series but it also blends with some past article we have here on The Nerd Nook as well as the latest edition of 3 Randoms where we covered Seaborn.

By the end, you’ll know exactly how to perform EDA in Python and why it’s such a key step in machine learning.

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Alright, let’s get off to the races nerds, enjoy this weeks article!

Can I Just Skip this Whole EDA Part?

I mean, you could but wouldn’t get very far. So I’m going to say no. Think of yourself as Gibbs from NCIS solving a case (did any of you guys binge watch that show too? 🤣). You wouldn’t make a decision without first looking at all the evidence, right? Data analysis works the same way.

Plus EDA is really nice because it allows us to bring our data to life, we are basically creating art through code with our data!